Frontiers in Plant Science | www.frontiersin

Edited by:Felipe Klein Ricachenevsky,

Federal University of Rio Grande, Brazil

Reviewed by:Marta Silva Lopes,

Institute of Agrifood Research andTechnology (IRTA), Spain

Dawei Xin,Northeast Agricultural University,

China

*Correspondence:Hui Liu

hui.liu@uwa.edu.auGuijun Yan

guijun.yan@uwa.edu.au

Specialty section:This article was submitted to

Plant Abiotic Stress,a section of the journal

Frontiers in Plant Science

Received: 11 May 2020Accepted: 11 August 2020Published: 28 August 2020

Citation:Lu L, Liu H, Wu Y and Yan G (2020)Development and Characterization of

Near-Isogenic Lines RevealingCandidate Genes for a Major

7AL QTL Responsible forHeat Tolerance in Wheat.Front. Plant Sci. 11:1316.

doi: 10.3389/fpls.2020.01316

ORIGINAL RESEARCHpublished: 28 August 2020

doi: 10.3389/fpls.2020.01316

Development and Characterization ofNear-Isogenic Lines RevealingCandidate Genes for a Major 7ALQTL Responsible for Heat Tolerancein WheatLu Lu1,2,3, Hui Liu1,2*, Yu Wu3 and Guijun Yan1,2*

1 Faculty of Science, UWA School of Agriculture and Environment, The University of Western Australia, Perth, WA, Australia,2 The UWA Institute of Agriculture, The University of Western Australia, Perth, WA, Australia, 3 Chengdu Institute of Biology,Chinese Academy of Sciences, Chengdu, China

Wheat is one of the most important food crops in the world, but as a cool-season crop, itis more prone to heat stress, which severely affects crop production and grain quality.Heat tolerance in wheat is a quantitative trait, and the genes underlying reportedquantitative trait loci (QTL) have rarely been identified. Near-isogenic lines (NILs) with acommon genetic background but differing at a particular locus could turn quantitativetraits into a Mendelian factor; therefore, they are suitable material for identifying candidategenes for targeted locus/loci. In this study, we developed and characterized NILs from twopopulations Cascades × Tevere and Cascades × W156 targeting a major 7AL QTLresponsible for heat tolerance. Molecular marker screening and phenotyping for SPADchlorophyll content and grain-yield-related traits confirmed four pairs of wheat NILs thatcontrasted for heat-stress responses. Genotyping the NILs using a 90K Infinium iSelectSNP array revealed five single nucleotide polymorphism (SNP) markers within the QTLinterval that were distinguishable between the isolines. Seven candidate genes linked tothe SNPs were identified as related to heat tolerance, and involved in important processesand pathways in response to heat stress. The confirmed multiple pairs of NILs andidentified candidate genes in this study are valuable resources and information for furtherfine-mapping to clone major genes for heat tolerance.

Keywords: heat tolerance, near-isogenic lines, 7AL, quantitative trait loci, single nucleotide polymorphism assay,candidate genes, wheat

INTRODUCTION

Crop growth and productivity are often limited by abiotic stresses, especially heat and drought(Priya et al., 2019). Wheat is one of the most important food grain crops in the world, but being acool-season crop, it often experiences heat stress. Each 1°C rise in temperature above the optimumcan cause a 3–5% reduction in single grain weight under controlled environments (Dawson andWardlaw, 1989) or field conditions (Wiegand and Cuellar, 1981). Phenology is known to confound

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crop responses to heat; therefore, the effect of heat stress dependson its timing, duration, and frequency (Rezaei et al., 2015; Ballaet al., 2019). While conventional breeding has developed someheat-tolerant lines, it is a time-consuming process, and thegenetic and physiological basis of the improvements remainunclear (Driedonks et al., 2016). Understanding the underlyingmechanism of heat tolerance and identifying candidate genes willhelp to accelerate the breeding of heat-stress-resilient genotypes(Budak et al., 2015).

Heat tolerance is a quantitative trait (Moffatt et al., 1990a; Yanget al., 2002) that involves complex genetic, physiological, andbiochemical controls and is affected by environmental factors.Numerous heat tolerance QTL have been identified; for example,Yang et al. (2002) found QTL on the short arms of chromosomes1B and 5A linked to grain filling duration; Mason et al. (2010,2011) reported several QTL for heat susceptibility indices andyield traits on chromosomes 1A, 1B, 2A, 2B, 3B, 5A, and 6D;Paliwal et al. (2012) reported QTL on chromosomes 2B, 7B, and7D for thousand-grain weight, grain fill duration, and canopytemperature depression, respectively; Vijayalakshmi et al. (2010)reported QTL on chromosomes 2A, 3A, 4A, 6A, 6B, and 7A withsignificant effects on grain yield, grain weight, grain filling, staygreen, and senescence-associated traits under post-anthesis high-temperature stress in wheat. Most of these reported QTL havebeen based on mapping using low-density simple sequence repeat(SSR) markers and/or amplified fragment length polymorphism(AFLP) markers. Talukder et al. (2014) increased the markerdensity to 972 molecular markers and identified QTL associatedwith different traits related to heat tolerance in wheat. They foundthat QTL QHtscc.ksu-7A on chromosome 7A was consistentlyidentified for traits thylakoid membrane damage (TMD), plasmamembrane damage (PMD), and SPAD chlorophyll content (SCC),with high logarithm of odds (LOD) values ranging from 4.15 to6.95 and explaining high phenotypic variations ranging from 18.9to 33.5%. This major QTL QHtscc.ksu-7A, with flanking markersXbarc121 and Xbarc49, was chosen as the target locus fordeveloping NILs in this study.

It remains challenging to use QTL markers directly inbreeding programs due to the large genomic intervals ofthe most identified QTL (Mia et al., 2019). One solution foridentifying candidate genes and closely linked markers is todevelop NILs, which turn quantitative traits into Mendelianfactors (Liu et al., 2006). NILs are pairs of lines that have thesame genetic background between isolines, except for thetargeted locus (Dorweiler et al., 1993). NILs make it easier tostudy phenotypic impacts attributable to a specific gene or locus(Pumphrey et al., 2007). Characterizing NILs through genotype–phenotype association analyses can lead to the identification ofcandidate genes (Mirdita et al., 2008; Liu et al., 2010; Mia et al.,2019; Wang et al., 2019). Traditionally, NILs development hasbeen considered time-consuming and tedious (Tuinstra et al.,1997). By combining a fast generation cycling system (FGCS)(Zheng et al., 2013) with heterogeneous inbred family (HIF)method and repeated DNA marker-assisted selection (MAS)(Tuinstra et al., 1997), the NIL development process can beshortened to about six generations per year (Yan et al., 2017).

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Single nucleotide polymorphism (SNP) markers are high-density DNA markers widely used in genetic studies, includinggenetic diversity, phylogenetic relationships, and marker-traitassociations, such as genome-wide association study (GWAS) orQTL mapping (Wang et al., 2014; Cabral et al., 2018). The 90k SNParray, developed from hexaploid wheat and Aegilops tauschiisequences (Wang et al., 2014) with dense coverage of the wheatgenome, has been extensively harnessed for genetic research(Cavanagh et al., 2013; Avni et al., 2014; Colasuonno et al., 2014;Russo et al., 2014; Sukumaran et al., 2015). Due to its efficiency ofcharacterizing genetic resources and discriminating between closelyrelated lines (Rimbert et al., 2018), 90k SNP genotyping was used inthis study to characterize the developed NILs, in combination withphenotyping under controlled environments.

The objectives of this study were to 1) develop and confirmNILs targeting the major heat tolerance QTL on chromosome7A, 2) identify candidate gene(s) underlying the 7A QTLresponsible for heat tolerance by genotypic and phenotypiccharacterization of the NILs, and 3) shed light on the geneticmechanism of heat tolerance in wheat by inspecting this majorgenomic region and investigating its underlying candidate genes.

MATERIALS AND METHODS

Plant Materials and Selection of CrossingParentsIn a previous study, 499 wheat genotypes from a variety ofsources were screened and evaluated for heat-stress responses(Hameed, 2015). Among them, cultivars Tevere and W156showed heat tolerance at both the seedling and reproductivestages with high yield, whereas Cascades (Aroona//Tadorna/Inia66) was sensitive at both stages (unpublished data). Cascades andTevere are two common wheat cultivars and W156 is a landrace,which originated from Australia, Italy, and India, respectively.When the flanking marker Xbarc49 of the targeted 7A QTL wasused for genotyping the three cultivars, heat-tolerant Tevere andW156 showed the tolerance allele at 216 bp, and heat-susceptibleCascades showed the susceptibility allele at 203 bp. The threecultivars were therefore used to establish two cross populations,Cascades/W156 and Cascades/Tevere, for the development ofNILs targeting the 7A locus.

Development of NILsNILs were developed from the two populations using the HIFmethod (Tuinstra et al., 1997) in combination with embryo-culture-based FGCS (Zheng et al., 2013; Yan et al., 2017),following a similar procedure as described in Wang et al.(2019). Specifically, MAS started from the second generation ofprogenies (F2) (Figure 1A) where genomic DNA was isolatedfrom two-week-old seedlings of each plant using a modifiedCTAB method (Murray and Thompson, 1980). Xbarc49 (Figure1B), the flanking marker of QHtscc.ksu-7A (Talukder et al.,2014), was used to identify heterozygous progenies (Rr) from thetwo cross populations (Figure 1C). PCR reactions wereperformed, and amplified products viewed following the

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protocol described in Wang et al. (2018). MAS of heterozygousprogenies, together with embryo-culture-based FGCS (Figures1D, E), continued until the eighth generation of progenies (F8);at F8, only those homozygous progenies from single seed descentwith the tolerance allele (RR/+) from either W156 or Tevere andthose with susceptibility allele (rr/–) from Cascades were selectedas pairs of candidate NILs. Thirteen pairs of F8 NILs, numberedfrom NIL1 to NIL13, developed from the two populations wereused as putative NIL pairs to screen their phenotypes forperformance under heat stress.

Plant Growth Conditions and HeatTreatmentsThe seeds of all isolines were germinated in water on Petri dishes,before sowing one plant per pot (8 cm × 8 cm × 16 cm)containing soil media (5:2:3 compost:peat:sand, pH 6.0) (Miaet al., 2019). For each isoline, six pots (three replicates each for

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the control and the heat-stress treatment) were grown in anaturally lit glasshouse at The University of Western Australia,Crawley, Western Australia (31°59’ S, 115°49’E). The plants werefertilized fortnightly with ‘Diamond Red’ (Campbells FertilisersAustralasia Pty Ltd, Australia) from four weeks after sowing untilthe end of the grain-filling period. The glasshouse environment(temperature, relative humidity, and light intensity) is detailed inSupplementary Table S1. The experiment was arranged in acompletely randomized block design.

Anthesis date, as Zadoks’ growth scale Z60 (Zadoks, 1974), ofeach plant was recorded by tagging each plant on the wheat headwhere the first anther appeared. The time point to start the heattreatment, treatment temperature, and other settings were as perprevious studies (Pradhan et al., 2012; Talukder et al., 2014;Shirdelmoghanloo et al., 2016a). Specifically, on the 10th dayafter anthesis (DAA), the three treatment pots were moved into agrowth chamber set to 37/27°C (day/night), 14-h photoperiod,

FIGURE 1 | (A) Process of the HIF method to develop NIL pairs, with percentage heterozygosity in each generation shown on the left of the flow chart. (B) QTLhotspot in wheat chromosome 7A and the marker (in the box) used for selection, adapted from Talukder et al., 2014. (C) Marker-assisted selection of differentprogeny types, with tolerance progeny, susceptible progeny, and heterozygous progeny marked RR, rr, and Rr, respectively, on the top of the gel bands. (D) Cultureof young embryos on Petri plates in a sterile medium. (E) Seedlings from embryo culture growing in the plant growth chamber.

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and 420 mmol m−2 s−1 light intensity for the 3-day heattreatment. Enough water was given to the plants to ensurethere was no drought stress, only heat stress. The pots werereturned to the glasshouse after the heat treatment.

Phenotype ScreeningChlorophyll contents were measured on flag leaves using ahandheld portable chlorophyll meter (SPAD-502Plus; KonicaMinolta, Osaka) for both the control and heat-stressed plants.Time points for measurements followed the procedures describedin Pradhan et al. (2012). Changes in SPAD chlorophyll contents(DSCC) in flag leaves were calculated based on differences inchlorophyll contents before (as ‘Chl10’ at 10 DAA) and after (as‘Chl13’ at 13 DAA) treatment, as follows: DSCC = mean of Chl10 –mean of Chl13 (Shirdelmoghanloo et al., 2016b). Measurementsand calculations were the same for controls.

Agronomic traits (grain number and grain yield per plant)were measured after harvest for plants in the control and heat-stressed treatments. The performance differences in final yieldbetween isoline pairs were determined by subtracting meanvalues in the control from those in the heat treatment.

Statistical analyses were undertaken using t-tests to comparephenotypic variation in the NIL pairs. True NILs were confirmedif significant differences existed in the performance of testedtraits between isolines, and their resistant and susceptiblephenotypes matched their genotypes of Xbarc49’s RR/+ andrr/ – alleles, respectively.

Genotyping by 90k Infinium iSelect SNPArrayThe 90K SNP array was used for genotyping, with genotype–phenotype associations used to identify candidate gene(s) (Alauxet al., 2018). Specifically, genomic DNA samples of the confirmedNILs were genotyped using the Wheat 90K Illumina iSelectArray (Wang et al., 2014). SNP clustering and genotype callingwere performed using GenomeStudio 2.0 software (Illumina).SNPs with a call frequency <0.8 (i.e., missing data points >20%),minor allele frequency (MAF) <0.05 or heterozygous calls >0.25were removed. SNP sequences that differed between NIL pairswere used to perform a BLAST search against the wheatreference genome (IWGSC, 2018). SNPs located on the 7AL

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chromosome arm, especially those within the marker interval ofQHtscc.ksu-7A (Talukder et al., 2014), were scrutinized usingJBrowse (http://www.wheatgenome.org/Tools-and-Resources/Sequences) for candidate gene discovery.

RESULTS

Four NIL Pairs Confirmed With SignificantDifferences in Chlorophyll ContentAfter comparing the DSCC data for NIL pairs, no significantdifferences were observed among the 13 putative NIL pairsgrown in the non-stressed (control) treatment, whereas theisolines of four NIL pairs significantly differed in the heat-stressed treatment. Of these, the isolines with positive alleles(+NILs) had smaller reductions in SPAD chlorophyll contentthan isolines with negative alleles (–NILs). The isolines of NILs 5,10, and 13 differed significantly at P < 0.05, and NIL 9 differedsignificantly at P < 0.01 (Table 1).

Tolerant Isolines Performed Better ThanSusceptible Isolines on Other AgronomicTraits in the Confirmed NlLsFurther investigation showed that significant differences existedbetween most of the treated isolines and their correspondingcontrols for yield-related traits. The differences or gaps in grainnumber per pot and yield per pot between the control and heat-stressed treatments are shown in Figure 2 to compare yieldperformance in tolerant and susceptible isolines. For grainnumber, three of the four +NILs (5, 9, and 10) had smaller gapsthan –NILs. For yield, all four +NILs had smaller gaps betweennon-stressed and stressed treatments than their counterparts.Generally, heat stress had smaller negative effects on grainnumber and yield in +NILs than –NILs.

Five SNP Markers With ConsistentContrasting Genotypes in the ConfirmedNIL PairsOf the 81,587 SNPs on the array, 53,052 were analyzed across the21 chromosomes after removing those that did not meet the

TABLE 1 | Reduction of SPAD chlorophyll content (DSCC) in the confirmed NIL pairs.

NIL name Population DSCC (Chl10–Chl13)

Control Treatment

Mean P-value Mean P-value

NIL5(+) Cascade/Tevere –0.4 ns 0.53 *NIL5(–) –0.8 7.13NIL9(+) Cascade/W156 1.1 ns 4.57 **NIL9(–) 5.05 7.97NIL10(+) Cascade/W156 0.67 ns 1.7 *NIL10(–) 0.6 12.23NIL13(+) Cascade/Tevere 0.15 ns 1.7 *NIL13 (–) 1.27 6.7

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ns = non-significant at P ≤ 0.05; * = significant at P ≤ 0.05; ** = significant at P ≤ 0.01. The statistics was done using t-test. (+) indicates isolines with resistance allele fromW156 or Tevere;(–) indicates isolines with susceptibility allele from Cascades.

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selection criteria. Analyzing the SNPs among the four confirmedpairs of isolines identified five SNPs within the 7AL QTL regionwith consistent contrasting genotypes between the resistant andsusceptible isolines (Table 2).

Candidate Genes Identified by Blasting theWheat Reference GenomeSeven candidate genes were identified within the QHtscc.ksu-7Aregion by blasting the above five SNP markers with wheatreference genome RefV1.0. The annotations for high and lowconfidence genes of RefV1.0 were used, with various databasescompared, including NCBI (https://www.ncbi.nlm.nih.gov) andInterMine (http://www.wheatgenome.org/Tools-and-Resources/Sequences), to determine possible gene functions (Table 3).

Markers BS00071558_51 and wsnp_Ku_c5160_9203226 wereco-located on gene TraesCS7A01G612600LC, with their SNPvariations in the intron and untranslated region (UTR),respectively (Figure 3). Markers wsnp_Ku_rep_c113718_96236830and wsnp_RFL_Contig2864_2688208 were co-located on geneTraesCS7A01G432000 and TraesCS7A01G431600, respectively,with their SNP variations in the UTR and exon regions. Althoughno gene was co-located on wsnp_Ra_c26491_36054023, the markerwas located close to genes TraesCS7A01G428200 (15,752 bp away)and TraesCS7A01G428400 (273,590 bp away), both of whichfunction as peroxidase.

Blasting the sequence of TraesCS7A01G612600LC in NCBIidentified five ESTs related to plant biotic or abiotic stress

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responses (Manickavelu et al., 2012). Genes TraesCS7A01G432000and TraesCS7A01G431600 were involved in important biochemistryor molecular functions, such as protein binding, Zinc finger, F-boxdomain, Kelch motif, and Per-Arnt-Sim (PAS) domain, all of whichare involved in various pathways of plant responses and reactions toexternal stimulus (Craig and Tyers, 1999; Adams et al., 2000; PragandAdams, 2003; Hefti et al., 2004; van den Burg et al., 2008; Henniget al., 2009; Moglich et al., 2009; Peng et al., 2012; Liu et al., 2015).

Apart from these genes, two other noteworthy genes—TraesCS7A01G430500, which functions as a sugar transporter familyprotein, and TraesCS7A01G430600, which functions as a heat shockprotein (HSP)—were located in the interval between markerwsnp_Ra_c26491_36054023 and wsnp_RFL_Contig2864_2688208.

DISCUSSION

In this study, we reported the development of 13 putative NIL pairstargeting a major locus for heat tolerance. Among them, four pairswere confirmed as true NILs by genotype–phenotype associationanalysis. The confirmed NILs showed differential responses undernon-stressed and heat-stressed conditions. NILs with alleles fromthe heat-tolerant parents (Tevere andW156) performed better thantheir counterparts in terms of physiological and agronomical traits,such as chlorophyll content, grain number, and grain yield.Characterization of these NIL pairs revealed that the presence ofthe tolerance allele significantly increased heat tolerance in the

TABLE 2 | SNPs showing consistent contrast callings in the confirmed four pairs of NILs.

No. Marker Name SNP Physical location

1 BS00071558_51 [T/C] chr7A:626897156.6268972562 wsnp_Ku_c5160_9203226 [T/C] chr7A:626897816.6268980163 wsnp_Ku_rep_c113718_96236830 [A/G] chr7A:625739519.6257397194 wsnp_Ra_c26491_36054023 [A/G] chr7A:621582917.6215830945 wsnp_RFL_Contig2864_2688208 [T/C] chr7A:625640069.625640169

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FIGURE 2 | Differences in agronomic trait gaps between the confirmed isolines of tolerant and susceptible alleles. Three replicates each for the control (naturally litglasshouse with conditions described in detail in Table S1) and the heat-stressed treatment (growth chamber set at 37/27°C day/night) were used for statisticalanalysis. The column indicates trait gaps between the treatment and control, with error bar on top. * indicates significant difference at P ≤ 0.05; ** indicatessignificant difference at P ≤ 0.01. Statistics done using t-test.

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plants. One reason for the higher grain yield and grain number inthe heat-tolerant NILs may be the positive correlation betweenchlorophyll content and gas exchange parameters reported inseveral studies (Chen et al., 2016; Wang et al., 2016).

Many physiological traits have been closely associated withheat response, such as canopy temperature, leaf senescence,night respiration, chlorophyll fluorescence, and cell membranethermo-stability (Narayanan, 2018). The original QTL targeted inthis study was associated with multiple traits, including thylakoidmembrane damage (TMD), plasma membrane damage (PMD),and SPAD chlorophyll content (SCC) (Talukder et al., 2014).Membrane thermostability has a strong genetic correlation withgrain yield in wheat (Reynolds et al., 1994; Fokar et al., 1998). Lossof chlorophyll content during grain filling has been associated

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with reduced yield under field conditions (Reynolds et al., 1994).TMD and PMD have also been associated with grain yield(Moffatt et al., 1990b; Saadall et al., 1990; Reynolds et al., 1994;Araus et al., 1998; Marcum, 1998; Blum et al., 2001; Wahid andShabbir, 2005). Strong correlations among these traits suggest thatthese traits are under pleiotropic genetic control.

Leaf chlorophyll content is a major indicator of thephotosynthetic capability of plant tissues (Rao et al., 2001;Pietrini et al., 2017). Some studies that focused on yield andphotosynthetic traits (Raven and Griffiths, 2015; Gaju et al., 2016;Merchuk-Ovnat et al., 2016) have shown that the photosyntheticfunction duration of leaves is closely correlated to grain yield inwheat. Furthermore, spectral characteristics measured by SPADare a good indicator for evaluating crop responses to high

TABLE 3 | Function annotations of candidate genes.

Gene Physical position Database Identifier Description

TraesCS7A01G612600LC 626894752.626898021 EMBL-EBI BQ245642HX055146HX055177CJ956871CJ945027

Yield improvement under stressresponse to blast funguswheat responses to fungi infectionsresponse to powdery mildew infectionresponses to fungi infections

TraesCS7A01G432000 625737761.625740656 Interpros IPR013083 Zinc finger, RING/FYVE/PHD-type

TraesCS7A01G431600 625635655.625640991 GOsInterpros

Pfams

GO:0005515IPR001810IPR015915IPR000014IPR011498PF13426PF13415PF07646PF13418PF12937

Molecular Function: protein bindingF-box domainKelch-type beta propellerPAS domainKelch-repeat type 2PAS domainGalactose oxidase, central domainKelch motifGalactose oxidase, central domainF-box-like

TraesCS7A01G428200 621564527.621567165 GOs

Interpros

Pfams

GO:0020037GO:0055114GO:0004601GO:0006979IPR000823IPR002016IPR010255IPR019793IPR019794PF00141

MF: heme bindingBP: oxidation-reduction processMF: peroxidase activityBP: response to oxidative stressPlant peroxidaseHaem peroxidase, plant/fungal/bacterialHaem peroxidasePeroxidases heam-ligand binding sitePeroxidase, active sitePeroxidase

TraesCS7A01G428400 621856684.621857860 Similar function as gene TraesCS7A01G428200

TraesCS7A01G430500 624959655.624961254 GOs

Interpros

Pfams

GO:0016021GO:0016020GO:0055085GO:0005215GO:0022857GO:0022891IPR020846IPR003663IPR005828IPR005829PF00083

CC: integral component of membraneCC: membraneBP: transmembrane transportMF: transporter activityMF: transmembrane transporter activityMF: substrate-specific transmembrane transporter activityMajor facilitator superfamily domainSugar/inositol transporterMajor facilitator, sugar transporter-likeSugar transporter, conserved siteSugar (and other) transporter

TraesCS7A01G430600 624965100.624967076 Interpros

Pfams

IPR013126IPR029047IPR029048IPR018181PF00012

Heat shock protein 70 familyHeat shock protein 70kD, peptide-binding domainHeat shock protein 70kD, C-terminal domainHeat shock protein 70, conserved siteHsp70 protein

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temperature (Talukder et al., 2014; Tao et al., 2016). Due to itscorrelation with yield and other performance indicators underheat stress, chlorophyll content measured by SPAD could beused as an easy and reasonable morphological marker forassessing heat tolerance, especially at the initial mass screeningstage. The smaller the reduction in SPAD chlorophyll content(DSCC), the more tolerant the plant should be to heat stress.SPADmeters have been used to estimate leaf chlorophyll contentin research and agricultural practices because it is a quick, simpleand non-destructive method (Novichonok et al., 2016; Padillaet al., 2018; Zhao et al., 2018; de Souza et al., 2019; Galanti et al.,2019; Zhang et al., 2019). Here, agronomic traits such as grainnumber and grain yield were also measured to further confirmthe true NIL pairs, which are valuable material for further studiesof post-anthesis heat tolerance in wheat.

Except for the report by Talukder et al. (2014), the major QTLQHtscc.ksu-7A targeted in this study has been identified asresponsible for heat tolerance in other studies. Vijayalakshmi et al.(2010) reported a 7A QTL linked to marker Xbarc121 for heat-tolerance traits, including Fv/Fm and time to maximum rate ofsenescence. Talukder et al. (2014) revealed that several ESTs, locatedin the same wheat deletion bin as Xbarc49, were related to stressresponse in different studies; therefore, the authors proposed thatthe major QTL QHtscc.ksu-7A was a genomic region rich ingenes related to stress response. Dixit et al. (2015) hypothesizedthat multiple genes underpinned large-effect QTL. In this study,seven candidate genes within the targeted major 7AL QTL wereidentified as responsible for heat tolerance post-anthesis. Thegene TraesCS7A01G612600LC is a homologous gene toTraesCS7A01G612600L; blasting TraesCS7A01G612600L by Blastx(translated nucleotide to protein) on NCBI with criteria ofpercentage identity ≥50% and e-value of <1e-5 revealed its originfrom Triticum urartu, and it has been up-regulated at 24 h osmoticstress in the ABA-dependent signaling pathway (Li et al., 2019).

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Therefore, the gene TraesCS7A01G612600LC identified in this studysupposedly has a similar function as its homologous gene. Abscisicacid (ABA) is generally considered as a stress signaling hormone,and the expression of stress-responsive genes in plants is primarilyregulated by ABA-dependent and ABA-independent pathways(Agarwal and Jha, 2010; Yoshida et al., 2014). The ABA-dependent pathway is central to osmotic-stress responses inplants (Li et al., 2019). Moreover, five expression sequence tag(EST) markers associated with TraesCS7A01G612600LC wererelated to plant biotic or abiotic stress responses. Among theseEST markers, HX055146, HX055177, CJ956871, and CJ945027were related to wheat responses to fungi infections, includingFusarium head blight and powdery mildew (Manickavelu et al.,2012), while BQ245642 encoded a polypeptide useful for yieldimprovement by improving plant growth and development underat least one stress condition (Kovalic et al., 2007).

The annotation of genes TraesCS7A01G432000 andTraesCS7A01G431600 revealed their involvement in someimportant biochemistry or molecular functions, such as proteinbinding, Zinc finger, F-box domain, Kelch motif, PAS domain,and galactose oxidase. F-box domain genes are related to plantresistance, while F-box proteins are associated with cellularfunctions, such as signal transduction and regulation of the cellcycle during plant vegetative and reproductive growth anddevelopment (Craig and Tyers, 1999). For example, F-box proteinFOA1 plays a role in ABA signaling involved in seed germination(Peng et al., 2012), ACRE189/ACIF1 regulates cell defense and deathwhen tomato and tobacco are attacked by pathogens (van den Burget al., 2008), Kelch motifs and kelch-repeat b-propellers undergo avariety of binding interactions with other molecules (Adams et al.,2000; Prag andAdams, 2003), and a PAS domain acts as amolecularsensor (Hennig et al., 2009; Moglich et al., 2009; Liu et al., 2015) andhas been deemed as the key structural motif involved in protein-protein interactions during physiological reactions (Hefti et al.,

FIGURE 3 | Candidate gene structures and SNPs between tolerant and susceptible NILs. The structural information of genes and SNP markers was extracted fromthe wheat genome database (https://urgi.versailles.inra.fr/jbrowseiwgsc/gmod_jbrowse). The SNP positions between tolerant and susceptible NILs are marked withan asterisk. For the first three genes, SNPs distinguishing tolerant and susceptible NILs were within the identified genes, either exon, intron, or untranslated region(UTR); for the other four genes, SNPs were outside the candidate genes and their physical distances to the genes are labeled.

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2004). In summary, all these biological structures are extensivelyinvolved in various pathways of plant responses and reactions toexternal stimuli such that we can deduce that genesTraesCS7A01G432000 and TraesCS7A01G431600 regulate heattolerance by controlling protein structures and protein binding onthe biological structures mentioned above to achieve signaltransduction, which is the key part in the pathway of heat tolerance.

The remaining four genes TraesCS7A01G428200 ,Trae sCS7A01G428400 , Trae sCS7A01G430500 , andTraesCS7A01G430600 have functions as peroxidase, peroxidase,sugar transporter family protein, and HSP, respectively. The sugartransporter family protein is related to yield as sugar transport is oneof the most important processes for plant development and theirresponses to biotic and abiotic factors (Lalonde et al., 2004; Lemoineet al., 2013). Tolerance to heat stress is frequently associated withmaintaining sugar content in source leaves (Vasseur et al., 2011;Zhou et al., 2017). For example, a heat-tolerant tomato (Solanumlycopersicum) genotype had significantly higher fructose andsucrose contents in mature leaves than a heat-sensitive genotypeunder heat stress (Zhou et al., 2017). Peroxidase is related to stresstolerance—peroxidase activities increased significantly in a heat-tolerant wheat genotype in response to heat treatment (Sairam et al.,1998). Exposure to heat stress often leads to the generation ofdestructive ROS (reactive oxygen species), but plants haveantioxidant mechanisms to escape excessive ROS. Several studieshave shown that peroxidase plays an important role in antioxidantmechanisms and ameliorates the effects of heat stress in wheat(Suzuki et al., 2011; Caverzan et al., 2016). HSPs play a pivotal roleas chaperones in conferring biotic and abiotic stress tolerance(Baniwal et al., 2004). The expression of HSP genes induced byhigh temperature can preserve the stability and function ofintracellular proteins and protect them from denaturationthrough protein folding. They enhance membrane stability and

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detoxify ROS by positively regulating the antioxidant enzymesystem. Additionally, HSP genes use ROS as a signal to moleculesto induce HSP production. HSP also enhances plant immunity byaccumulating and stabilizing pathogenesis-related proteins undervarious biotic stresses (Haq et al., 2019). Genotypes generating HSPscan withstand heat stress as they protect proteins from heat-induceddamage (Farooq et al., 2011).

The gene structure analysis (Figure 3) identified several SNPsdistinguishing tolerant and susceptible NILs located withinthe candidate genes, in which one marker wsnp_RFL-Contig2864_2688208 falls in the exon region. These markers canbe used as functional markers, because not only does the exonencodes protein functional units but also noncoding DNA includingintron and UTR has played significant roles in many studies (Cobbet al., 2008; Khurana et al., 2013; Lu et al., 2015; Grünewald et al.,2015). SNPs outside the identified candidate genes can still be usedin marker-assisted selection for genetic and breeding research.Aharon et al. (2016) explored how far SNPs can be from theaffected genes using a pathway-based approach and found thataffected genes were often up to 2Mbps from the associated SNP andnot necessarily the closest to the SNP.

The molecular mechanisms underlying heat tolerance inwheat remain unclear. A schematic network, proposed toexplain the mechanism of heat tolerance in legumes, suggestedthat signalling and metabolic pathways, involving a series ofphysiochemical processes and important molecules such asHSPs, antioxidants, metabolites, and hormones, play key rolesin regulating the legume response to heat stress (Liu et al., 2019).The candidate genes identified in this study are consistent, to alarge extent, with their proposed network. Therefore, wehypothesize that such a pathway might exist in wheat, wheremany key genes collaboratively regulate the crop’s response toheat stress (Figure 4).

FIGURE 4 | Postulated pathway based on the findings of this study showing a collaborative regulation network of multiple genes in wheat in response to heatstress. Signaling pathway and metabolic pathway involving a series of physiochemical processes and important molecules, including HSPs, antioxidants, metabolites,and hormones.

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CONCLUSIONS

The NILs developed and validated in this study confirmed thatthe 7AL QTL, QHtscc.ksu-7A, is a major locus responsible forheat tolerance in wheat. The confirmed NILs and identifiedcandidate genes are valuable resources for future studies in finemapping and functional analyses of the chromosome region toclone the underlying gene(s).

DATA AVAILABILITY STATEMENT

All datasets presented in this study are included in the article/supplementary material.

AUTHOR CONTRIBUTIONS

LL, HL, and GY conceived and designed the study. HL developedNIL populations, and LL conducted the experiments in the plantgrowth chambers and greenhouse. LL collected the relevant data andperformed the analysis with guidance fromGY andHL. LL preparedthe manuscripts with inputs from YW, HL, and GY. All authorscontributed to the article and approved the submitted version.

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FUNDING

This research was funded by the Global Innovation LinkagesProject (GIL53853) from the Australian Department of Industry,Innovation, and Science. The first author acknowledges theResearch Training Program Scholarship from the AustralianGovernment for sponsoring her Ph.D. study. The Universityof Western Australia provided funds for the open accesspublication fee.

ACKNOWLEDGMENTS

The authors would like to thank Professor Jacqueline Batley andDr. Aneeta Pradhan for assistance in the 90k SNP arraygenotyping. The authors also thank Dr. Christine Davies atTweak Editing for correcting English language errors.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found onlineat: https://www.frontiersin.org/articles/10.3389/fpls.2020.01316/full#supplementary-material.

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Conflict of Interest: The authors declare that the research was conducted in theabsence of any commercial or financial relationships that could be construed as apotential conflict of interest.

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August 2020 | Volume 11 | Article 1316